Department Events
WEB MINING AND ANALYTICS
Course Objectives:
This course enables the students to
- Introduce students to the basic concepts and techniques of Information Retrieval, Web Search, Data Mining, and Machine Learning for extracting knowledge from the web.
- Describe complex data types with respect to spatial and web mining
- Appreciate the use of machine learning approaches for Web Content Mining
- Describe the various aspects of web usage mining
- Develop skills of using recent data mining software for solving practical problems of Web Mining
- Interpret emergent features such as the structure and evolution of the Web graph, its traffic patterns, and the spread of information
Course Outcomes(COs):
Learning Outcome (at course level)
| Learning and teaching strategies | Assessment Strategies |
| Approach in teaching: Interactive Lectures, Discussion, Demonstration, Experiment
Learning activities for the students: Self-learning assignments, Quiz activity, presentation, flip classroom, |
|
Introduction
Introduction – Web Mining – Theoretical background –Algorithms and techniques –
Association rule mining – Sequential Pattern Mining -Information retrieval and Web search – Information retrieval Models-Relevance Feedback- Text and Web page Pre-processing
Web Content Mining
Web Content Mining – Supervised Learning – Decision tree - Naive Bayesian Text
Classification -Support Vector Machines - Ensemble of Classifiers. Unsupervised Learning - K-means Clustering -Hierarchical Clustering –Partially Supervised Learning
Web Structure and Web Usage Mining
Hyperlink based Ranking – Introduction -Social Networks Analysis- Co-Citation and Bibliographic Coupling - Page Rank -Authorities -Enhanced Techniques for Page Ranking - Community Discovery – Web Crawling -A Basic Crawler Algorithm- Implementation Issues
Web Usage Mining – sources of data- Applications -Click stream Analysis -Web Server Log Files - Data Collection and Pre Processing- Cleaning and Filtering- Data Modeling for Web Usage Mining – Issues- Discovery and Analysis of Web Usage Patterns – Used tools in Web Usage mining.
Introduction to web analytics
Motivation and historical perspective on the development of web analytics, Display and search advertising , Knowledge discovery from web data, Major computing paradigms, Typical problem formulations
Web analytics at e-Business scale
Framework for mapping business needs to web analytics tasks, Data collection architecture, Introduction to OLAP, Web data exploration and reporting, Introduction to Splunk
Essential Readings:
- Bing Liu, “ Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)”, Springer; 2nd Edition 2009
Suggested Readings:
- Guandong Xu ,Yanchun Zhang, Lin Li, “Web Mining and Social Networking: Techniques and Applications”, Springer; 1st Edition.2010
- Zdravko Markov, Daniel T. Larose, “Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage”, John Wiley & Sons, Inc., 2007
E-resources:
- Data Mining and Analysis( https://online.stanford.edu/)
- Text Mining and Analytics ( https://www.coursera.org/ )
- Text Retrieval and Search Engines ( https://www.coursera.org/ )
- Data Visualization( https://www.coursera.org/ )
Journals (International / National):
- International Journal of Information Technology and Decision Making
- International Journal of Mining Science and Technology
- Social Network Analysis and Mining
- International Journal of Engineering Research & Technology (IJERT)
